Laurent Charlin

108 papers A* 20A 7Journal 63Unranked 17

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Contextual Preference Distribution Learning. CoRR Benjamin Hudson, Laurent Charlin, Emma Frejinger
2026	J	`jnl`	Modular Memory is the Key to Continual Learning Agents. CoRR Vaggelis Dorovatas, Malte Schwerin, Andrew D. Bagdanov, Lucas Caccia, Antonio Carta, Laurent Charlin, Barbara Hammer, Tyler L. Hayes, Timm Hess, Christopher Kanan, Dhireesha Kudithipudi, Xialei Liu, Vincenzo Lomonaco, Jorge Mendez-Mendez, Darshan Patil, Ameya Prabhu, Elisa Ricci, Tinne Tuytelaars, Gido M. van de Ven, Liyuan Wang, Joost van de Weijer, Jonghyun Choi, Martin Mundt, Rahaf Aljundi
2026	J	`jnl`	Optimizing User Profiles via Contextual Bandits for Retrieval-Augmented LLM Personalization. CoRR Linfeng Du, Ye Yuan, Zichen Zhao, Fuyuan Lyu, Emiliano Penaloza, Xiuying Chen, Zipeng Sun, Jikun Kang, Laurent Charlin, Xue Liu, Haolun Wu
2026	J	`jnl`	Privileged Information Distillation for Language Models. CoRR Emiliano Penaloza, Dheeraj Vattikonda, Nicolas Gontier, Alexandre Lacoste, Laurent Charlin, Massimo Caccia
2026	J	`jnl`	Self-Supervised Learning from Structural Invariance. CoRR Yipeng Zhang, Hafez Ghaemi, Jungyoon Lee, Shahab Bakhtiari, Eilif B. Muller, Laurent Charlin
2025	J	`jnl`	AInstein: Assessing the Feasibility of AI-Generated Approaches to Research Problems. CoRR Shambhavi Mishra, Gaurav Sahu, Marco Pedersoli, Laurent Charlin, Jose Dolz, Christopher Pal
2025	A*	`conf`	Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization. ICML Emiliano Penaloza, Tianyue H. Zhang, Laurent Charlin, Mateo Espinosa Zarlenga
2025	J	`jnl`	Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization. CoRR Emiliano Penaloza, Tianyue H. Zhang, Laurent Charlin, Mateo Espinosa Zarlenga
2025	J	`jnl`	Audio Prototypical Network For Controllable Music Recommendation. CoRR Firat Öncel, Emiliano Penaloza, Haolun Wu, Shubham Gupta, Mirco Ravanelli, Laurent Charlin, Cem Subakan
2025	—	`conf`	Audio Prototypical Network for Controllable Music Recommendation. MLSP Firat Öncel, Emiliano Penaloza, Haolun Wu, Shubham Gupta, Mirco Ravanelli, Laurent Charlin, Cem Subakan
2025	J	`jnl`	How to Train Your LLM Web Agent: A Statistical Diagnosis. CoRR Dheeraj Vattikonda, Santhoshi Ravichandran, Emiliano Penaloza, Hadi Nekoei, Megh Thakkar, Thibault Le Sellier de Chezelles, Nicolas Gontier, Miguel Muñoz-Mármol, Sahar Omidi Shayegan, Stefania Raimondo, Xue Liu, Alexandre Drouin, Laurent Charlin, Alexandre Piché, Alexandre Lacoste, Massimo Caccia
2025	J	`jnl`	LitLLMs, LLMs for Literature Review: Are we there yet? Trans. Mach. Learn. Res. Shubham Agarwal, Gaurav Sahu, Abhay Puri, Issam H. Laradji, Krishnamurthy Dj Dvijotham, Jason Stanley, Laurent Charlin, Christopher Pal
2025	J	`jnl`	ReviewerToo: Should AI Join The Program Committee? A Look At The Future of Peer Review. CoRR Gaurav Sahu, Hugo Larochelle, Laurent Charlin, Christopher Pal
2025	A*	`conf`	TEARS: Text Representations for Scrutable Recommendations. WWW Emiliano Penaloza, Olivier Gouvert, Haolun Wu, Laurent Charlin
2024	J	`jnl`	Discovering Data Structures: Nearest Neighbor Search and Beyond. CoRR Omar Salemohamed, Laurent Charlin, Shivam Garg, Vatsal Sharan, Gregory Valiant
2024	—	`conf`	Integrating Present and Past in Unsupervised Continual Learning. CoLLAs Yipeng Zhang, Laurent Charlin, Richard S. Zemel, Mengye Ren
2024	J	`jnl`	Integrating Present and Past in Unsupervised Continual Learning. CoRR Yipeng Zhang, Laurent Charlin, Richard S. Zemel, Mengye Ren
2024	J	`jnl`	LLMs for Literature Review: Are we there yet? CoRR Shubham Agarwal, Gaurav Sahu, Abhay Puri, Issam H. Laradji, Krishnamurthy Dj Dvijotham, Jason Stanley, Laurent Charlin, Christopher Pal
2024	J	`jnl`	LitLLM: A Toolkit for Scientific Literature Review. CoRR Shubham Agarwal, Issam H. Laradji, Laurent Charlin, Christopher Pal
2024	J	`jnl`	Operational Research: methods and applications. J. Oper. Res. Soc. Fotios Petropoulos, Gilbert Laporte, Emel Aktas, Sibel A. Alumur, Claudia Archetti, Hayriye Ayhan, Maria Battarra, Julia A. Bennell, Jean-Marie Bourjolly, John E. Boylan, Michèle Breton, David Canca, Laurent Charlin, Bo Chen, Cihan Tugrul Cicek, Louis Anthony Cox, Christine S. M. Currie, Erik Demeulemeester, Li Ding, Stephen M. Disney, Matthias Ehrgott, Martin J. Eppler, Günes Erdogan, Bernard Fortz, L. Alberto Franco, Jens Frische, Salvatore Greco, Amanda J. Gregory, Raimo P. Hämäläinen, Willy Herroelen, Mike Hewitt, Jan Holmström, John N. Hooker, Tugçe Isik, Jill Johnes, Bahar Yetis Kara, Özlem Karsu, Katherine Kent, Charlotte Köhler, Martin H. Kunc, Yong-Hong Kuo, Adam N. Letchford, Janny Leung, Dong Li, Haitao Li, Judit Lienert, Ivana Ljubic, Andrea Lodi, Sebastián Lozano, Virginie Lurkin, Silvano Martello, Ian G. McHale, Gerald Midgley, John D. W. Morecroft, Akshay Mutha, Ceyda Oguz, Sanja Petrovic, Ulrich Pferschy, Harilaos N. Psaraftis, Sam Rose, Lauri Saarinen, Saïd Salhi, Jing-Sheng Song, Dimitrios Sotiros, Kathryn E. Stecke, Arne K. Strauss, Istenç Tarhan, Clemens Thielen, Paolo Toth, Tom Van Woensel, Greet Vanden Berghe, Christos Vasilakis, Vikrant Vaze, Daniele Vigo, Kai Virtanen, Xun Wang, Rafal Weron, Leroy White, Mike Yearworth, E. Alper Yildirim, Georges Zaccour, Xuying Zhao
2024	J	`jnl`	TEARS: Textual Representations for Scrutable Recommendations. CoRR Emiliano Penaloza, Olivier Gouvert, Haolun Wu, Laurent Charlin
2024	A*	`conf`	Towards Modular LLMs by Building and Reusing a Library of LoRAs. ICML Oleksiy Ostapenko, Zhan Su, Edoardo M. Ponti, Laurent Charlin, Nicolas Le Roux, Lucas Caccia, Alessandro Sordoni
2024	J	`jnl`	Towards Modular LLMs by Building and Reusing a Library of LoRAs. CoRR Oleksiy Ostapenko, Zhan Su, Edoardo Maria Ponti, Laurent Charlin, Nicolas Le Roux, Matheus Pereira, Lucas Caccia, Alessandro Sordoni
2023	—	`conf`	Challenging Common Assumptions about Catastrophic Forgetting and Knowledge Accumulation. CoLLAs Timothée Lesort, Oleksiy Ostapenko, Pau Rodríguez, Diganta Misra, Md Rifat Arefin, Laurent Charlin, Irina Rish
2023	—	`conf`	From IID to the Independent Mechanisms assumption in continual learning. AAAI Bridge Program Oleksiy Ostapenko, Pau Rodríguez, Alexandre Lacoste, Laurent Charlin
2023	J	`jnl`	Improving the generalizability and robustness of large-scale traffic signal control. CoRR Tianyu Shi, François-Xavier Devailly, Denis Larocque, Laurent Charlin
2023	A*	`conf`	Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network. NeurIPS Tristan Deleu, Mizu Nishikawa-Toomey, Jithendaraa Subramanian, Nikolay Malkin, Laurent Charlin, Yoshua Bengio
2023	J	`jnl`	Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network. CoRR Tristan Deleu, Mizu Nishikawa-Toomey, Jithendaraa Subramanian, Nikolay Malkin, Laurent Charlin, Yoshua Bengio
2023	—	`conf`	Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges. CoLLAs Massimo Caccia, Jonas Mueller, Taesup Kim, Laurent Charlin, Rasool Fakoor
2023	J	`jnl`	Towards Compute-Optimal Transfer Learning. CoRR Massimo Caccia, Alexandre Galashov, Arthur Douillard, Amal Rannen-Triki, Dushyant Rao, Michela Paganini, Laurent Charlin, Marc'Aurelio Ranzato, Razvan Pascanu
2022	J	`jnl`	A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions. CoRR François St-Hilaire, Dung Do Vu, Antoine Frau, Nathan Burns, Farid Faraji, Joseph Potochny, Stephane Robert, Arnaud Roussel, Selene Zheng, Taylor Glazier, Junfel Vincent Romano, Robert Belfer, Muhammad Shayan, Ariella Smofsky, Tommy Delarosbil, Seulmin Ahn, Simon Eden-Walker, Kritika Sony, Ansona Onyi Ching, Sabina Elkins, Anush Stepanyan, Adela Matajova, Victor Chen, Hossein Sahraei, Robert Larson, Nadia Markova, Andrew Barkett, Laurent Charlin, Yoshua Bengio, Iulian Vlad Serban, Ekaterina Kochmar
2022	J	`jnl`	Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes. CoRR Mizu Nishikawa-Toomey, Tristan Deleu, Jithendaraa Subramanian, Yoshua Bengio, Laurent Charlin
2022	—	`conf`	Continual Learning with Foundation Models: An Empirical Study of Latent Replay. CoLLAs Oleksiy Ostapenko, Timothée Lesort, Pau Rodríguez, Md Rifat Arefin, Arthur Douillard, Irina Rish, Laurent Charlin
2022	J	`jnl`	Foundational Models for Continual Learning: An Empirical Study of Latent Replay. CoRR Oleksiy Ostapenko, Timothée Lesort, Pau Rodríguez, Md Rifat Arefin, Arthur Douillard, Irina Rish, Laurent Charlin
2022	J	`jnl`	IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control. IEEE Trans. Intell. Transp. Syst. François-Xavier Devailly, Denis Larocque, Laurent Charlin
2022	J	`jnl`	Learning To Cut By Looking Ahead: Cutting Plane Selection via Imitation Learning. CoRR Max B. Paulus, Giulia Zarpellon, Andreas Krause, Laurent Charlin, Chris J. Maddison
2022	A*	`conf`	Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning. ICML Max B. Paulus, Giulia Zarpellon, Andreas Krause, Laurent Charlin, Chris J. Maddison
2022	J	`jnl`	Model-based graph reinforcement learning for inductive traffic signal control. CoRR François-Xavier Devailly, Denis Larocque, Laurent Charlin
2022	J	`jnl`	Scaling the Number of Tasks in Continual Learning. CoRR Timothée Lesort, Oleksiy Ostapenko, Diganta Misra, Md Rifat Arefin, Pau Rodríguez, Laurent Charlin, Irina Rish
2022	J	`jnl`	Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline. CoRR Massimo Caccia, Jonas Mueller, Taesup Kim, Laurent Charlin, Rasool Fakoor
2022	J	`jnl`	The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights. CoRR Maxime Gasse, Quentin Cappart, Jonas Charfreitag, Laurent Charlin, Didier Chételat, Antonia Chmiela, Justin Dumouchelle, Ambros M. Gleixner, Aleksandr M. Kazachkov, Elias B. Khalil, Pawel Lichocki, Andrea Lodi, Miles Lubin, Chris J. Maddison, Christopher Morris, Dimitri J. Papageorgiou, Augustin Parjadis, Sebastian Pokutta, Antoine Prouvost, Lara Scavuzzo, Giulia Zarpellon, Linxin Yang, Sha Lai, Akang Wang, Xiaodong Luo, Xiang Zhou, Haohan Huang, Sheng Cheng Shao, Yuanming Zhu, Dong Zhang, Tao Quan, Zixuan Cao, Yang Xu, Zhewei Huang, Shuchang Zhou, Binbin Chen, Minggui He, Hao Hao, Zhiyu Zhang, Zhiwu An, Kun Mao
2021	—	`conf`	A Comparative Study of Learning Outcomes for Online Learning Platforms. AIED (2) François St-Hilaire, Nathan Burns, Robert Belfer, Muhammad Shayan, Ariella Smofsky, Dung Do Vu, Antoine Frau, Joseph Potochny, Farid Faraji, Vincent Pavero, Neroli Ko, Ansona Onyi Ching, Sabina Elkins, Anush Stepanyan, Adela Matajova, Laurent Charlin, Yoshua Bengio, Iulian Vlad Serban, Ekaterina Kochmar
2021	A*	`conf`	Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations. ICCV Pau Rodríguez, Massimo Caccia, Alexandre Lacoste, Lee Zamparo, Issam H. Laradji, Laurent Charlin, David Vázquez
2021	J	`jnl`	Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations. CoRR Pau Rodríguez, Massimo Caccia, Alexandre Lacoste, Lee Zamparo, Issam H. Laradji, Laurent Charlin, David Vázquez
2021	J	`jnl`	Comparative Study of Learning Outcomes for Online Learning Platforms. CoRR François St-Hilaire, Nathan Burns, Robert Belfer, Muhammad Shayan, Ariella Smofsky, Dung Do Vu, Antoine Frau, Joseph Potochny, Farid Faraji, Vincent Pavero, Neroli Ko, Ansona Onyi Ching, Sabina Elkins, Anush Stepanyan, Adela Matajova, Laurent Charlin, Yoshua Bengio, Iulian Vlad Serban, Ekaterina Kochmar
2021	A*	`conf`	Continual Learning via Local Module Composition. NeurIPS Oleksiy Ostapenko, Pau Rodríguez, Massimo Caccia, Laurent Charlin
2021	J	`jnl`	Continual Learning via Local Module Composition. CoRR Oleksiy Ostapenko, Pau Rodríguez, Massimo Caccia, Laurent Charlin
2021	A*	`conf`	Pretraining Representations for Data-Efficient Reinforcement Learning. NeurIPS Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, R. Devon Hjelm, Philip Bachman, Aaron C. Courville
2021	J	`jnl`	Pretraining Representations for Data-Efficient Reinforcement Learning. CoRR Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, R. Devon Hjelm, Philip Bachman, Aaron C. Courville
2021	J	`jnl`	Sequoia: A Software Framework to Unify Continual Learning Research. CoRR Fabrice Normandin, Florian Golemo, Oleksiy Ostapenko, Pau Rodríguez, Matthew D. Riemer, Julio Hurtado, Khimya Khetarpal, Dominic Zhao, Ryan Lindeborg, Timothée Lesort, Laurent Charlin, Irina Rish, Massimo Caccia
2021	—	`conf`	The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights. NeurIPS (Competition and Demos) Maxime Gasse, Simon Bowly, Quentin Cappart, Jonas Charfreitag, Laurent Charlin, Didier Chételat, Antonia Chmiela, Justin Dumouchelle, Ambros M. Gleixner, Aleksandr M. Kazachkov, Elias B. Khalil, Pawel Lichocki, Andrea Lodi, Miles Lubin, Chris J. Maddison, Christopher Morris, Dimitri J. Papageorgiou, Augustin Parjadis, Sebastian Pokutta, Antoine Prouvost, Lara Scavuzzo, Giulia Zarpellon, Linxin Yang, Sha Lai, Akang Wang, Xiaodong Luo, Xiang Zhou, Haohan Huang, Sheng Cheng Shao, Yuanming Zhu, Dong Zhang, Tao Quan, Zixuan Cao, Yang Xu, Zhewei Huang, Shuchang Zhou, Binbin Chen, Minggui He, Hao Hao, Zhiyu Zhang, Zhiwu An, Kun Mao
2020	—	`conf`	A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM. AIED (2) Iulian Vlad Serban, Varun Gupta, Ekaterina Kochmar, Dung Do Vu, Robert Belfer, Joelle Pineau, Aaron C. Courville, Laurent Charlin, Yoshua Bengio
2020	J	`jnl`	A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM. CoRR Iulian Vlad Serban, Varun Gupta, Ekaterina Kochmar, Dung Do Vu, Robert Belfer, Joelle Pineau, Aaron C. Courville, Laurent Charlin, Yoshua Bengio
2020	A	`conf`	Causal Inference for Recommender Systems. RecSys Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei
2020	J	`jnl`	IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control. CoRR François-Xavier Devailly, Denis Larocque, Laurent Charlin
2020	A*	`conf`	Language GANs Falling Short. ICLR Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau, Laurent Charlin
2020	—	`conf`	Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles. EMNLP (1) Yao Lu, Yue Dong, Laurent Charlin
2020	J	`jnl`	Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles. CoRR Yao Lu, Yue Dong, Laurent Charlin
2020	—	`conf`	On the Effectiveness of Two-Step Learning for Latent-Variable Models. MLSP Y. Cem Sübakan, Maxime Gasse, Laurent Charlin
2020	A*	`conf`	Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning. NeurIPS Massimo Caccia, Pau Rodríguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Page-Caccia, Issam Hadj Laradji, Irina Rish, Alexandre Lacoste, David Vázquez, Laurent Charlin
2020	J	`jnl`	Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning. CoRR Massimo Caccia, Pau Rodríguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Caccia, Issam H. Laradji, Irina Rish, Alexandre Lacoste, David Vázquez, Laurent Charlin
2020	A*	`conf`	Synbols: Probing Learning Algorithms with Synthetic Datasets. NeurIPS Alexandre Lacoste, Pau Rodríguez López, Frederic Branchaud-Charron, Parmida Atighehchian, Massimo Caccia, Issam Hadj Laradji, Alexandre Drouin, Matt Craddock, Laurent Charlin, David Vázquez
2020	J	`jnl`	Synbols: Probing Learning Algorithms with Synthetic Datasets. CoRR Alexandre Lacoste, Pau Rodríguez, Frédéric Branchaud-Charron, Parmida Atighehchian, Massimo Caccia, Issam H. Laradji, Alexandre Drouin, Matt Craddock, Laurent Charlin, David Vázquez
2019	—	`conf`	Continual Learning of New Sound Classes Using Generative Replay. WASPAA Zhepei Wang, Y. Cem Sübakan, Efthymios Tzinis, Paris Smaragdis, Laurent Charlin
2019	J	`jnl`	Continual Learning of New Sound Classes using Generative Replay. CoRR Zhepei Wang, Y. Cem Sübakan, Efthymios Tzinis, Paris Smaragdis, Laurent Charlin
2019	A*	`conf`	Exact Combinatorial Optimization with Graph Convolutional Neural Networks. NeurIPS Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi
2019	J	`jnl`	Exact Combinatorial Optimization with Graph Convolutional Neural Networks. CoRR Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi
2019	A*	`conf`	Online Continual Learning with Maximal Interfered Retrieval. NeurIPS Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, Massimo Caccia, Min Lin, Lucas Page-Caccia
2019	J	`jnl`	Online Continual Learning with Maximally Interfered Retrieval. CoRR Rahaf Aljundi, Lucas Caccia, Eugene Belilovsky, Massimo Caccia, Min Lin, Laurent Charlin, Tinne Tuytelaars
2019	A	`conf`	Session-Based Social Recommendation via Dynamic Graph Attention Networks. WSDM Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, Jian Tang
2019	J	`jnl`	Session-based Social Recommendation via Dynamic Graph Attention Networks. CoRR Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, Jian Tang
2018	J	`jnl`	A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version. Dialogue Discourse Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau
2018	A*	`conf`	Focused Hierarchical RNNs for Conditional Sequence Processing. ICML Nan Rosemary Ke, Konrad Zolna, Alessandro Sordoni, Zhouhan Lin, Adam Trischler, Yoshua Bengio, Joelle Pineau, Laurent Charlin, Christopher J. Pal
2018	J	`jnl`	Focused Hierarchical RNNs for Conditional Sequence Processing. CoRR Nan Rosemary Ke, Konrad Zolna, Alessandro Sordoni, Zhouhan Lin, Adam Trischler, Yoshua Bengio, Joelle Pineau, Laurent Charlin, Chris Pal
2018	J	`jnl`	Language GANs Falling Short. CoRR Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau, Laurent Charlin
2018	J	`jnl`	The Deconfounded Recommender: A Causal Inference Approach to Recommendation. CoRR Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei
2018	A*	`conf`	Towards Deep Conversational Recommendations. NeurIPS Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, Chris Pal
2018	J	`jnl`	Towards Deep Conversational Recommendations. CoRR Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, Chris Pal
2017	A*	`conf`	A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues. AAAI Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin, Joelle Pineau, Aaron C. Courville, Yoshua Bengio
2017	—	`conf`	A Sparse Probabilistic Model of User Preference Data. Canadian AI Matthew Smith, Laurent Charlin, Joelle Pineau
2017	J	`jnl`	Learnable Explicit Density for Continuous Latent Space and Variational Inference. CoRR Chin-Wei Huang, Ahmed Touati, Laurent Dinh, Michal Drozdzal, Mohammad Havaei, Laurent Charlin, Aaron C. Courville
2017	J	`jnl`	Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks. CoRR Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Laurent Charlin, Chris Pal, Yoshua Bengio
2017	J	`jnl`	Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus. Dialogue Discourse Ryan Thomas Lowe, Nissan Pow, Iulian Vlad Serban, Laurent Charlin, Chia-Wei Liu, Joelle Pineau
2016	J	`jnl`	A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues. CoRR Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin, Joelle Pineau, Aaron C. Courville, Yoshua Bengio
2016	A	`conf`	Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence. RecSys Dawen Liang, Jaan Altosaar, Laurent Charlin, David M. Blei
2016	J	`jnl`	Generative Deep Neural Networks for Dialogue: A Short Review. CoRR Iulian Vlad Serban, Ryan Lowe, Laurent Charlin, Joelle Pineau
2016	A*	`conf`	How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation. EMNLP Chia-Wei Liu, Ryan Lowe, Iulian Serban, Michael Noseworthy, Laurent Charlin, Joelle Pineau
2016	J	`jnl`	How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation. CoRR Chia-Wei Liu, Ryan Lowe, Iulian Vlad Serban, Michael Noseworthy, Laurent Charlin, Joelle Pineau
2016	A*	`conf`	Modeling User Exposure in Recommendation. WWW Dawen Liang, Laurent Charlin, James McInerney, David M. Blei
2016	—	`conf`	On the Evaluation of Dialogue Systems with Next Utterance Classification. SIGDIAL Conference Ryan Lowe, Iulian Vlad Serban, Michael Noseworthy, Laurent Charlin, Joelle Pineau
2016	J	`jnl`	On the Evaluation of Dialogue Systems with Next Utterance Classification. CoRR Ryan Lowe, Iulian Vlad Serban, Michael Noseworthy, Laurent Charlin, Joelle Pineau
2015	J	`jnl`	A Survey of Available Corpora for Building Data-Driven Dialogue Systems. CoRR Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau
2015	A	`conf`	Deep Exponential Families. AISTATS Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David M. Blei
2015	A	`conf`	Dynamic Poisson Factorization. RecSys Laurent Charlin, Rajesh Ranganath, James McInerney, David M. Blei
2015	J	`jnl`	Dynamic Poisson Factorization. CoRR Laurent Charlin, Rajesh Ranganath, James McInerney, David M. Blei
2015	J	`jnl`	Modeling User Exposure in Recommendation. CoRR Dawen Liang, Laurent Charlin, James McInerney, David M. Blei
2015	—	`—`	Supervised and Active Learning for Recommender Systems. Laurent Charlin
2014	—	`conf`	Content-based recommendations with Poisson factorization. NIPS Prem Gopalan, Laurent Charlin, David M. Blei
2014	J	`jnl`	Deep Exponential Families. CoRR Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David M. Blei
2014	A*	`conf`	Leveraging user libraries to bootstrap collaborative filtering. KDD Laurent Charlin, Richard S. Zemel, Hugo Larochelle
2013	—	`conf`	Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning. ICML (3) Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, Richard S. Zemel
2012	J	`jnl`	A Framework for Optimizing Paper Matching CoRR Laurent Charlin, Richard S. Zemel, Craig Boutilier
2012	J	`jnl`	Active Learning for Matching Problems CoRR Laurent Charlin, Richard S. Zemel, Craig Boutilier
2012	A*	`conf`	Active Learning for Matching Problems. ICML Laurent Charlin, Richard S. Zemel, Craig Boutilier
2012	J	`jnl`	Hierarchical POMDP Controller Optimization by Likelihood Maximization CoRR Marc Toussaint, Laurent Charlin, Pascal Poupart
2011	A	`conf`	A Framework for Optimizing Paper Matching. UAI Laurent Charlin, Richard S. Zemel, Craig Boutilier
2008	A	`conf`	Hierarchical POMDP Controller Optimization by Likelihood Maximization. UAI Marc Toussaint, Laurent Charlin, Pascal Poupart
2006	—	`conf`	Automated Hierarchy Discovery for Planning in Partially Observable Environments. NIPS Laurent Charlin, Pascal Poupart, Romy Shioda